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Multiscale Deep Convolutional Networks for Characterization and Detection of Alzheimer's Disease Using MR images

Conference paper
Authors Chenjie Ge
Qixun Qu
Irene Yu Hua Gu
Asgeir Store Jakola
Published in Proceedings - International Conference on Image Processing, ICIP
ISSN 15224880
Publication year 2019
Published at Institute of Neuroscience and Physiology, Department of Clinical Neuroscience
Language en
Links https://doi.org/10.1109/ICIP.2019.8...
Keywords Alzheimer's disease detection, feature fusion and enhancement, MR images, multiscale CNN, multiscale features
Subject categories Medical Image Processing, Radiology, Nuclear Medicine and Medical Imaging, Neurology

Abstract

© 2019 IEEE. This paper addresses the issues of Alzheimer's disease (AD) characterization and detection from Magnetic Resonance Images (MRIs). Many existing AD detection methods use single-scale feature learning from brain scans. In this paper, we propose a multiscale deep learning architecture for learning AD features. The main contributions of the paper include: (a) propose a novel 3D multiscale CNN architecture for the dedicated task of AD detection; (b) propose a feature fusion and enhancement strategy for multiscale features; (c) empirical study on the impact of several settings, including two dataset partitioning approaches, and the use of multiscale and feature enhancement. Experiments were conducted on an open ADNI dataset (1198 brain scans from 337 subjects), test results have shown the effectiveness of the proposed method with test accuracy of 93.53%, 87.24% (best, average) on subject-separated dataset, and 99.44%, 98.80% (best, average) on random brain scan-partitioned dataset. Comparison with eight existing methods has provided further support to the proposed method.

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